how can we study the evolution of animal minds? · 43" deeper understanding of how animal...
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How can we study the evolution of animal minds? 1
Maxime Cauchoix12* , Alexis Chaine3 2
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Maxime Cauchoix, 1Department of Biology, University of Ottawa, Ottawa, Canada 4 2 Institute for Advanced study in Toulouse, Toulouse, France 5
Alexis S. Chaine, 3 Station for Experimental Ecology in Moulis, CNRS, Moulis, France 6
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Correspondence: 8
Dr. Maxime Cauchoix 9
Institute for Advanced study in Toulouse 10
21 allée de Brienne 11
31015 Toulouse Cedex 6 12
France 13
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Abstract 17
During the last 50 years, comparative cognition and neurosciences have improved our 18
understanding of animal minds while evolutionary ecology has revealed how selection acts on 19
traits through evolutionary time. We describe how this evolutionary approach can be used to 20
understand the evolution of animal cognition. We recount how comparative and fitness methods 21
have been used to understand the evolution of cognition and outline how these methods could be 22
extended to gain new insights into cognitive evolution. The fitness approach, in particular, offers 23
unprecedented opportunities to study the evolutionary mechanisms responsible for variation in 24
cognition within species and could allow us to investigate both proximate (ie: neural and 25
developmental) and ultimate (ie: ecological and evolutionary) underpinnings of animal cognition 26
together. Our goal in this review is to build a bridge between cognitive neuroscientist and 27
evolutionary biologists, illustrate how their research could be complementary, and encourage 28
evolutionary ecologists to include explicit attention to cognitive processes in their studies of 29
behaviour. We believe that in doing so, we can break new ground in our understanding of the 30
evolution of cognition as well as gain a much better understanding of animal behaviour. 31
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Introduction 35
Niko Tinbergen (Tinbergen, 1963) proposed that biologists should try to understand animal 36
behaviours in the light of two different and complimentary perspectives: the proximate and 37
ultimate (see Bateson and Laland, 2013; Laland et al., 2011 for recent updates). While both 38
approaches have been employed in the study of animal cognition, most studies have done so 39
independently with little integration across fields. After some promising, integrative studies in 40
the 1980s and 1990s (see Kamil, 1998 for a review), the last decades have seen the establishment 41
of entirely independent lines of research with only a few notable exceptions. We now have a 42
deeper understanding of how animal minds work, but we know very little about the evolution of 43
or ecological pressures that shape cognition. Consequently, we know very little about what role 44
cognition, a collection of highly plastic and flexible traits, plays in adaptation and biological 45
evolution. We believe the time is ripe for evolutionary ecology studies to explicitly integrate 46
cognition to generate a much stronger understanding of how the mind evolves. 47
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Proximate studies focus on the mechanisms underlying given behaviours and the developmental 49
biology of key structures. What stimuli trigger behaviours? How do neurons in the brain encode 50
stimuli and transform them into behaviour? What is the ontogeny of behaviour? In other words, 51
the proximate approach tries to understand how animal minds work. The current view for 52
cognitive neuroscientists is that the animal mind emerges from brain activity as the neural 53
machinery encodes, manipulates, stores and recalls information, which is together called 54
‘cognition’. Cognition emerges when the brain transforms information into mental constructs or 55
representations (Barsalou, 2014). For cognitive scientists, cognition is a synonym of ‘mind’, 56
which, operationally, is divided in various cognitive functions, each function being implied in a 57
specific step of information processing (see also Figure 1). Perception (i.e. vision, olfaction, 58
audition, gustation and somesthesia) all contribute to the process by which mental 59
representations are built from environmental stimulation. Learning is the ability to associate 60
previously unrelated mental representations. Memory is the ability to store mental 61
representations either for a small amount of time (short term memory), a large amount of time 62
(long term memory) or in relation to a particular on-going task (working memory). Attention is 63
the mechanism allowing an individual to focus on only some mental representations among 64
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many. Decision-making is the process enabling an individual to compare mental representations 65
and choose the most appropriate given the environmental context. Finally, executive functions 66
(reasoning, problem solving, flexibility, categorization etc…) enable an individual to perform 67
operations and manipulations of mental representations. Cognition is also sometimes divided 68
according to the nature of the representation; one can for instance talk about spatial or social 69
cognition. 70
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The association between studies in psychology and neurosciences along with the advent of 72
powerful new neuroimaging technics (e.g. In vivo electrophysiology, Magnetic Resonance 73
Imaging (MRI), Positron emission Tomography (PET), optogenetic etc.) has lead us to better 74
understand how behaviours and decisions are linked to neural structures and neural activity in 75
several animal species including humans. Despite this in depth understanding, much less 76
progress has been made in understanding the evolutionary processes that have lead to the 77
patterns of cognition that we see. 78
79
Ultimate approaches focus on the evolutionary history of behaviours or traits and the selective 80
pressures that favour the evolution of those traits. Those using this approach have focused on 81
behaviours with only a few rare studies examining cognition per se (e.g. Bond and Kamil, 2002, 82
2006; Lyon, 2003; Théry and Casas, 2002). Evolutionary biologists and behavioural ecologists 83
have been primarily interested in the ecology and evolution of behaviour without examining the 84
cognitive mechanisms underlying these behaviours. What ecological or social contexts are 85
responsible for the evolution of a specific behaviour? What role does evolutionary history 86
(inheritance from a common ancestor) play in the evolution of that trait? What are the costs and 87
benefits of behaviours and what do they imply for selection on the animal’s life history strategy? 88
To answer these questions behavioural ecologists have adopted the Neo-Darwinian theoretical 89
framework and developed tools and models to understand the extreme variability of behaviours 90
within and among species. However, this approach focuses on the aggregate outcome of 91
cognition and action (i.e. the behaviour) and has usually considered the animal mind as a black 92
box (Giraldeau, 2004). Indeed, much of behavioral or evolutionary ecology theory is based on 93
strategic decision-making. While in some cases these strategic decisions reflect physiological 94
trade-offs, many more cases reflect decisions made probably on the basis of processing external 95
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information gathered by an individual. Attention in such studies is placed on the quality of 96
information and the outcome of a decision, but there is little understanding of how information is 97
processed and how cognitive abilities enhance or constrain decisions based on the available 98
information (Rowe, 1999, 2013). For example, social behavior, individual recognition, mate 99
choice, parental care, dispersal, foraging, and predator avoidance nearly always rely on gathering 100
external information. How well an individual gathers that information, how well it remembers 101
that information, and how it integrates different sources of information all depend on cognitive 102
capacities. To illustrate this notion (Figure 1) we can imagine a female who must choose the best 103
mate among males that each display a number of ornaments linked to various qualities (e.g. good 104
genes, parental care, nest defense, etc…). How does a female integrate the information provided 105
in each of the male’s sexual signals with information about the external ecological environment 106
(e.g. are there many nest predators)? As the female comparison shops for the best male, how 107
many of the males can she remember? If she chooses to return to the second male she saw, will 108
she remember where he is and will she recognize him? This example illustrates just some of the 109
cognitive processes related to one behavior that would have fundamental consequences for 110
sexual selection theory. Many other behaviors and life history strategies will similarly depend on 111
cognitive capacities and actual measurement of cognitive abilities has the potential to 112
fundamentally alter our views of behavior. 113
114
Understanding the evolutionary and ecological significance of cognition has been a major 115
challenge in biology as highlighted in several recent books (Dukas and Ratcliffe, 2009; Heyes 116
and Huber, 2000; Shettleworth, 2010) and review articles (Boogert et al., 2011; Dukas, 2004, 117
2008; Healy and Braithwaite, 2000; Kamil, 1998; MacLean et al., 2012; Pravosudov and 118
Smulders, 2010; Real, 1993; Thornton et al., 2012) and has led to a new field of research called 119
cognitive ecology. We argue that two factors will help to significantly advance our understanding 120
of animal cognition: 1) proximate and ultimate studies should develop lines of research that 121
allow direct integration of the two fields and 2) that evolutionary studies begin to apply their 122
research methods to cognition per se along with the behaviours that result from cognitive 123
processes. In doing so, we will gain a better understanding of how cognitive systems evolve and 124
how cognitive structures and function relate to the problems they evolved to solve. 125
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In this review, we focus more on the contribution that evolutionary biology can offer cognitive 127
research since much less work has been done in this domain. Despite this bias towards what 128
evolutionary biologists could contribute (i.e. what we know less about), we also highlight new 129
contributions that cognitive neuroscientists could make to better integrate proximate and ultimate 130
understandings of cognition. In the first section, we review past work testing popular hypotheses 131
for cognitive evolution using comparative methods and highlight future directions to exploit 132
using these methods. We then illustrate how measuring selection on cognition within a species 133
provides a great opportunity to better understand the evolution of cognition and create direct 134
links with proximate studies of cognition (e.g. neurosciences, cognitive-psychology). We finish 135
by presenting two lines of research as case studies—food hoarding and brood parasitism—that, 136
in our view, have best integrated ecological challenges, natural behaviour and underlying 137
cognitive adaptation and which could serve as examples for future cognitive ecology research. 138
139
Phylogenetic comparative studies of cognitive evolution 140
Current tests of factors that influence the evolution of the brain have largely relied on 141
comparative methods. The phylogenetic comparative approach (Felsenstein, 1985, 2008; 142
Felsenstein and Felenstein, 2004; Grafen, 1989; Harvey and Pagel, 1991; Ridley and Grafen, 143
1996) allows us to ask questions about how the evolution of a trait occurs through comparison of 144
extant species (although fossil evidence can be incorporated) while taking into account shared 145
ancestry estimated from a phylogeny. We can then ask questions such as what factors (e.g. social 146
or ecological) are associated with the evolution of a trait (e.g. brain size), if that trait evolves 147
directionally, how much common ancestry constrains evolution, and how the evolution of a trait 148
influences speciation rates. 149
150
The three major hypotheses of neurocognitive evolution that have been proposed focus on 151
identifying primary factors that have driven differences in brain size and cognitive function 152
across species. The first set of hypotheses suggest that cognition has evolved due to the value of 153
ecological intelligence; the ability to find and extract food (Byrne, 1997; Parker and Gibson, 154
1977), manage high spatiotemporal variation in food resources (Sol et al., 2005), or manage and 155
defend large territories (Clutton-Brock and Harvey, 1980). The second set of hypotheses propose 156
that cognition has evolved primarily due to its value in social intelligence; the ability to negotiate 157
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and succeed through dominance in large groups (Dunbar, 1998; Whiten and Byrne, 1988) or 158
alternatively the ability to manage positive relationships and social partnerships (Dunbar and 159
Shultz, 2007, 2010; Emery et al., 2007). The third hypothesis, recently proposed to reconcile 160
ecological and social drivers, suggests that cognition evolved to buffer individuals against 161
environmental challenges by producing appropriate behavioural responses in new socio-162
ecological contexts (Allman and Hasenstaub, 1999; Deaner et al., 2003; Sol, 2009). 163
Each of these hypotheses has been tested using comparative methods and each has found some 164
support. For example, brain size depends on diet in mammals (Eisenberg and Wilson, 1978; 165
Gittleman, 1986; Harvey et al., 1980; MacLean et al., 2014) suggesting a role of ecology. 166
Likewise, brain size and neocortex size are related to social group size (Barton and Dunbar, 167
1997; Dunbar, 1998; Dunbar and Bever, 1998; Gittleman, 1986; Marino, 1996) and other metrics 168
of social group structure in mammals (reviewed in Dunbar and Shultz, 2007) suggesting that 169
social drivers are also important to the evolution of the brain and cognition. Interestingly, 170
comparison of ecological and social factors in ungulates, showed that relative brain size is 171
influenced by social and ecological factors while relative neocortex size is only influenced by 172
sociality (Shultz and Dunbar, 2006). Finally, species with larger brains have been shown to 173
survive better in novel environments (Sol et al., 2005, 2007, 2008) in support to the cognitive 174
buffer hypothesis (Sol, 2009). 175
Comparative studies focused on brain size have also been largely criticised (Healy and Rowe, 176
2007; Lihoreau et al., 2012; Roth et al., 2010a). The high cognitive capacity of small-brained 177
invertebrates, such as bees and ants, suggests that high cognitive capabilities do not require large 178
overall brain size (Chittka and Niven, 2009). Measurements of brain size or brain structure 179
volumes are too coarse grained given that current neuroscience methods enable us to study fine 180
scale brain organisation and function (Healy and Rowe, 2007; Roth et al., 2010a). For instance, 181
cognitive neurosciences have revealed different brain networks and mechanism associated with 182
different cognitive abilities. Thus, instead of studying whole brain or neocortex size, comparative 183
studies should focus on neural circuits and functioning that are known to be involved in the 184
cognitive mechanism of interest when possible (Lihoreau et al., 2012). 185
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Efforts to address the problem that brain size may not be the same as cognitive abilities have 187
been made along two lines of comparative research: (i) spontaneous records of cognition-based 188
behaviours (e.g. innovation) in the wild and (ii) comparative psychology experiments in the lab. 189
The first line of research, also called ‘taxonomical counts of cognition in the wild’ (reviewed in 190
Lefebvre, 2011), enables the study of large samples of “spontaneous” behaviour occurring in the 191
selective environment or at least a natural or semi-natural habitat. This approach has confirmed 192
that relative brain size increases with increased tool use and frequency of innovation in birds 193
(Lefebvre et al., 1997, 2004) and primates (Lefebvre et al., 2004; Reader and Laland, 2002), 194
social learning in primates (Reader and Laland, 2002), or deception in primates (Byrne, 2004). 195
196
Taking the second approach, a few studies have begun comparing specific cognitive tasks among 197
a small number of related species that differ in social or ecological conditions. One of the most 198
advanced research programs of this kind, has been conduced on North American corvids (Balda 199
and Kamil, 2002; Balda et al., 1996; Kamil, 1998) using a large number of cognitive tests run in 200
the lab. Corvid species that rely heavily on food storing in the wild, such as Clark’s Nutcrakers 201
(Nucifraga columbiana), typically outperform other corvids in tasks requiring spatial cognition 202
(Olson et al., 1995); on the other hand, corvid species that are highly social, such as Pinyon Jays 203
(Gymnorhinus cyanocephalus), are better in cognition tasks mimicking social challenges such as 204
those designed to evaluate social learning, behavioural flexibility or transitive inference (Bond et 205
al., 2003, 2007, 2010; Templeton et al., 1999). Studies in primates have similarly addressed how 206
social structure is related to the evolution of cognitive abilities. Comparing species that differ in 207
their degree of sociality, Amici et al. (2008) have shown that species with fission-fusion social 208
organisation outperform species with very stable social groups in cognitive tasks requiring 209
inhibitory control and/or flexibility. Very recently, one of the most accomplished studies 210
merging phylogenetic and experimental cognition methods draws a slightly different picture 211
(MacLean et al., 2014). MacLean and his 57 collaborators realized the feat of gathering cognitive 212
performances of 36 animal species (from birds to rodents to apes) in two problem solving tasks 213
measuring self-control. Their results suggest that the major proximate mechanism underlying the 214
evolution of self control is the absolute brain volume rather than residual brain volume corrected 215
for body mass. They also report a significant relationship between cognitive performance and 216
dietary breadth but not social organization in primates. Thus, this massive comparative cognition 217
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study challenges both the proxy of cognition (relative brain size) and the hypothesis (social brain 218
hypothesis) tested in many brain comparative studies and illustrates the danger of over 219
interpreting comparative cognition studies. Continued efforts to link specific cognitive functions 220
to their ecological and social settings present a promising avenue to understand the evolution of 221
cognition while recognizing that different cognitive abilities may evolve under different 222
environmental contexts. 223
224
A number of new directions using the comparative method have still not been sufficiently 225
exploited. First and foremost, analyses should begin to compare specific regions of the brain or 226
brain function rather than coarse measures of brain size. The increasing ease of using new 227
technology (e.g. MRI, PET) to measure brain structures, connectivity, and function that are 228
frequently measured in cognitive neurosciences could provide another link between the evolution 229
of cognitive processes and ecological or social factors that influence cognition. Second, only a 230
small range of questions using comparative methods have been addressed (see MacLean et al., 231
2012 for a review). For example, comparative methods can be used to examine the sequence of 232
events in coevolution such that we could ask if the increase of a cognitive ability generally 233
precedes or succeeds specific social or ecological changes. Likewise, we could examine the 234
relative rates of evolution during the increase or decrease of a particular cognitive ability. Finally, 235
we can ask how shifts in cognition are associated with the speciation process itself (e.g. 236
Nicolakakis et al., 2003). Does the evolution of increased cognitive ability facilitate speciation? 237
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Intraspecific selection on neurocognitive traits: the fitness approach 239
Measuring contemporary selection has proved a powerful approach to understanding the 240
evolution of traits and this method could be readily applied to the evolution of cognition. The 241
basic premise of this ‘fitness’ approach follows Darwin’s theory of evolution (1859) which 242
suggests that short term selection is the primary cause of evolutionary change and speciation. 243
Therefore a careful examination of selection can help us understand how a trait evolves. 244
Selection can come from a number of origins which largely fall under natural selection, which 245
includes the effects of abiotic influences and interspecific interactions on survival and 246
reproduction (Darwin, 1859; and modern synthesis in Huxley, 1942), or social selection (Lyon 247
and Montgomerie, 2012; West-Eberhard, 1983), which includes selection due to intraspecific 248
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social interactions including the effects of mating behaviour (i.e. sexual selection, Darwin, 1871) 249
and kin cooperation (i.e. kin selection, Hamilton, 1964) among other intraspecific interactions. 250
There are two distinct advantages to the fitness method relative to the comparative method for 251
studying neurocognitive evolution. The first advantage is that studies of selection measure fitness 252
costs and benefits of specific traits which can provide a close match with measurements of 253
cognitive abilities and brain mechanisms currently studied in animal cognition and neurosciences 254
(Figure 2). Thus the fitness approach provides opportunities to integrate our proximate 255
understanding of cognition with new findings on the ultimate causes of cognitive evolution. The 256
second advantage is that examination of selection ideally includes identification of the agent of 257
selection or the specific social or ecological challenges that favour a specific trait. Adopting this 258
approach helps us acknowledge that there may be multiple factors that select for a given 259
cognitive ability and that each species will require only a subset of all cognitive skills given their 260
environment. 261
262
To show that animal cognition evolves under direct natural or social selection requires that the 263
three necessary conditions for selection and evolution that Darwin (1859, 1871) outlined apply to 264
cognitive abilities (Dukas, 2004). Traits, or in this case cognitive abilities, will evolve if (1) there 265
is variability in cognition between individuals, (2) that this variability in cognitive ability is 266
heritable, and (3) that this variation is related to variance in fitness (survival, reproductive 267
success) under specific environmental conditions. Few studies have tackled these questions 268
specifically, but evidence from the literature supports the notion that cognition should evolve 269
under selection making the fitness approach fruitful. 270
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(1) Variation in neurocognitive ability 272
Inter-individual variability in animal cognition studies is rarely reported, yet without variation, 273
cognition can not evolve. Studies in animal cognition generally focus on a small number of 274
individuals because of the time involved in training and testing subjects and this small sample 275
size precludes useful estimates of variation in cognitive abilities. However, a recent meta-276
analysis of variation in individual performances at three common cognitive tasks for different 277
species revealed very high inter-individual variability (Thornton and Lukas, 2012). Individual 278
performances varied almost continuously from 25-100% success at a task in tests for species 279
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with the largest sample sizes. Some of this variation is influenced by age, sex, developmental 280
conditions, or previous experience, so determining the extent of variation due to additive genes 281
rather than plasticity will require large sample sizes at single cognitive tasks. 282
283
Despite little direct evidence, there are a number of indirect measures of cognitive variability that 284
further support the notion that intraspecific variation in cognitive abilities should be widespread. 285
A growing number of recent studies focus on intraspecific variation in brain size including both 286
within and among population variation (for a review see Gonda et al., 2013). This variation is 287
also apparent in humans where inter-individual variation in brain structure and function has often 288
been considered “noise” until recently (Kanai and Rees, 2011). Perhaps the best evidence of 289
inter-individual cognitive variation comes from research on “general intelligence” in humans, 290
which has been extensively documented through the use of intelligence or ‘IQ’ tests and shows 291
high variation among individuals (Deary et al., 2010). Recent work has sought to tie variation 292
between IQ in humans to its neural substrate (Deary et al., 2010; Penke et al., 2012). 293
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(2) Heritability of neurocognitive abilities 295
Heritability of traits is difficult to measure since many non-genetic effects (common environment, 296
parental care, maternal effects, etc…) contribute to resemblance between parents and offspring. 297
For example, twin studies show that brain structure or function (e.g. face recognition) is heritable 298
in humans (Peper et al., 2007; Wilmer et al., 2010; Zhu et al., 2010), yet non-genetic effects that 299
occur in utero can not be excluded (but see Trzaskowski et al., 2013). One of the most powerful 300
approaches to demonstrate that heritability of cognitive traits exists is through artificial selection 301
experiments where species show phenotypic changes in response to researcher imposed selection 302
criteria. Mery and Kawecki have shown that associative learning abilities for choice of 303
oviposition substrate can be inherited in Drosophila melanogaster (see Kawecki, 2010 for a 304
review; Mery and Kawecki, 2002, 2003, 2005). Marked differences in learning and memory 305
were shown between high learning and low learning selected Drosophila populations over 15 306
generations. Artificial selection of brain size in guppies (Poecilia reticulata) also suggests 307
heritability of brain size (Kotrschal et al., 2013a) with a divergence in relative brain size of 9% 308
between lines selected for large vs. small size over just two generations. Interestingly, large-309
brained females outperformed small-brained females in a numerical learning test, which also 310
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provides evidence for an association between increased brain size and higher cognition. These 311
results should be treated cautiously since disentangling true heritability from plasticity would 312
require more than 2 generations and a relaxation of selection to see if brain size differences 313
persist (see Healy and Rowe, 2013; Kotrschal et al., 2013b). Finally, the use of genome wide 314
association has recently been used to demonstrate a genetic basis of human general intelligence 315
and cognition. This approach has shown that a substantial proportion (between 40 and 66%) of 316
individual differences in human general intelligence is linked to genetic variation (Davies et al., 317
2011; Benyamin et al., 2013; but see Chabris et al., 2012; Deary et al., 2012; Plomin et al., 2013). 318
319
(3) The fitness benefits of cognition 320
Selection on cognitive abilities will occur if there are fitness benefits to particular cognitive 321
phenotypes under a given set of environmental conditions. Addressing this question is 322
challenging because it requires both an estimate of cognitive performance or brain 323
structure/activity of a large number of individuals as well as fitness estimates, such as 324
reproductive success or survival, for the same individuals. Most cognitive tests are run under 325
laboratory conditions to control confounding effects on cognition and yet the best estimates of 326
fitness benefits should be measured in the wild where the importance of a specific cognitive 327
ability will also depend on the environmental context. Fitness measured in artificial selection 328
experiments on cognition or brain size have reported costs and benefits of improved cognitive 329
abilities in insects (Dukas, 2008; Kawecki, 2010) or increased brain size in fishes (Kotrschal et 330
al., 2013a), but the value of these traits in nature are unknown. In humans, general intelligence is 331
correlated with school achievement, job performance, health, and survival (Deary et al., 2010), 332
but not necessarily actual fitness (i.e. number of lifetime offspring that reproduce). 333
334
Two very recent studies have finally succeeded in measuring fitness consequences of problem-335
solving abilities in wild populations of great tits (Parus major) (Cauchard et al., 2012; Cole et al., 336
2012). Cole et al. (2012) took birds into short term captivity to perform an innovation task to get 337
food. Birds who solved the task had larger clutch sizes, but tended to desert their nest more often 338
if disturbed (Cole et al. 2012). Cauchard et al. (2013) conducted cognitive tests in the wild, 339
where birds had to remove an obstacle that blocked access to their nestbox. Those who could 340
solve the puzzle had higher survival of offspring to fledging. Both studies found individual 341
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variation in cognitive performance of birds (solvers vs. non solvers), so selection should act on 342
problem solving abilities. Fitness costs of higher cognition (e.g. higher desertion rates; Cole et al. 343
2012), could produce a trade-off that helps maintain variation in cognitive abilities among 344
individuals. These results are very promising, and should be diversified to a much broader range 345
of cognitive abilities and expanded to measures of brain structure or function (Figure 2). 346
Furthermore, following pioneering research linking food hoarding behaviour and spatial memory 347
(see Pravosudov and Roth II, 2013 for a review, and see Case Study 1 below), understanding 348
why cognition evolves will also require us to directly link cognitive performance (e.g. memory) 349
to ecological challenges that the animals face in their natural environment (e.g. finding a food 350
store). This last point is critical because if there are correlations among different cognitive 351
abilities then measurement of selection (i.e. higher fitness) on one ability could be due to 352
correlational selection on a different cognitive trait that is the actual target of selection. 353
354
Case Studies 355
As detailed above, there is now some evidence for selection of cognitive abilities in wild animals, 356
including humans. The next challenge for cognitive ecology is to identify which cognitive 357
functions are critical for a species in their natural environment. While for most species we are 358
still at the point of forming hypotheses on which cognitive abilities are critical (as we did for 359
mate choice in Figure 1), there are a few studies that have moved well beyond this stage. Here 360
we present two lines of research as examples of successfully linking natural behaviour, cognitive 361
function and ecological agents of selection. 362
363
(1) The evolutionary ecology of spatial memory 364
Food hoarding animals rely on food caching and later retrieval of caches to survive winter and 365
should have evolved excellent spatial memory abilities and associated neural structures (i.e. 366
hippocampus). Based on this simple ecology-driven hypothesis, a flourishing literature on the 367
cognitive ecology of food storing has emerged over the last thirty years. This work has 368
successfully combined proximate and ultimate understandings of spatial cognition and serves as 369
an example for future studies of the evolutionary ecology of cognition (see Brodin, 2010 for an 370
historical review). 371
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The first studies of the evolutionary and ecological significance of spatial memory employed the 372
comparative framework, with the prediction that scatter food hoarding species should surpass 373
non-hoarding species in spatial memory tasks and should have a relatively bigger hippocampus. 374
However, results from these early studies were equivocal and difficult to interpret. The 375
superiority of spatial capabilities in hoarding species was not always clear (reviewed in Healy et 376
al., 2009). Furthermore, and more concerning, the comparative approach suffers from a number 377
of confounding factors, such as morphological differences between species, that could never 378
clearly be separated from performance in cognitive tasks (but see (Kamil, 1998) for methods). 379
380
Problems with comparative analyses have been very elegantly solved by focusing on intra-381
specific variation in a number of landmark studies comparing populations exposed to different 382
ecological contexts. In one of the earliest of such studies, Pravosudov and Clayton (2002) 383
demonstrated that black-capped chickadees (Poecile atricapilla) living in harsh winter climates 384
(i.e. Alaska) cache more food, have higher spatial memory capabilities, and have a larger 385
hippocampus that contains more neurones than individuals of the same species in populations 386
from milder climates (i.e. Colorado). While the appearance of adaptation is clear, such 387
differences could reflect either local adaptation shaped by natural selection or result from 388
plasticity in brain structure and behaviour generated from the local environment. The persistence 389
of among population differences in brain structure and caching behaviour in common garden 390
experiments, during which 10 days-old chicks from these different populations were hand-raised 391
in identical environmental conditions, strongly argues for a role of natural selection in shaping 392
local adaptation for spatial memory, neural density, and neurogenesis in the hippocampus (Roth 393
et al., 2010b, 2012). 394
395
Recent analyses using this within species comparative approach in this and other species have 396
further pushed our understanding of the links between cognition and evolutionary ecology and 397
between proximate and ultimate understandings of cognitive evolution. Research in mountain 398
chickadees (Poecile gambeli) along an altitudinal gradient has shown similar patterns of 399
differentiation in food storage, spatial memory, and hippocampal characteristics as with 400
contrasted populations in the black-capped chickadee (Freas et al., 2013). Other studies have 401
extended this work on spatial memory differences across populations in caching behaviour to 402
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15
differences between behavioural strategies within a population (LaDage et al., 2013). In side-403
blotched lizards (Uta stansburiana), males adopt one of three different mating strategies that rely 404
to different degrees on spatial memory for territory defence and the distribution of available 405
females across territories. Accordingly, characteristics of the dorsal cortex and hippocampus 406
show differences among genetically determined alternative male strategies within a population 407
(Ladage et al., 2009; LaDage et al., 2013). Work on hippocampal size contrasts among 408
populations has recently been extended by fine scale studies of neural structure (Roth et al., 409
2010a, 2012) and differential gene expression (Pravosudov et al., 2013) within the hippocampus 410
among contrasted populations of birds. The next step should be to measure the influence of 411
spatial cognition and the underlying hippocampal structures or function on fitness in these 412
contrasted environments. 413
414
(2) Cognitive mechanisms of host-parasite arm races in brood parasites 415
Avian brood parasites lay their eggs in the nest of other individuals from the same or different 416
species to avoids the costs of parental care but imposes a cost on the host (reviewed in Davies, 417
2011). These reciprocal selection pressures have often led to an arms race of detection and 418
mimicry in egg appearance – a true cognitive battleground. Studies of avian brood parasitism 419
provide measures of selection on cognitive traits (recognition, rejection, deception), clear 420
identification of the agent of selection, examination of how cognition influences the 421
coevolutionary arms race, and neural traits associated with host-parasite life history. 422
423
Studies of avian brood parasitism have done an outstanding job of quantifying the fitness costs 424
and benefits to each player of the host-parasite arms race—often linked to recognition of 425
parasites (Davies, 2011; Lyon and Eadie, 2008). A parasite’s fitness is so intricately tied to 426
acceptance by hosts that they must adapt to new host defences either by identifying and changing 427
to a new host or surpassing host defences. Hosts, on the other hand, pay a cost of parasitism, but 428
the evolution of new defences (often a cognitive ability) must be balanced against the frequency 429
of parasitism and the costs of producing better defences (Davies and Brooke, 1988, 1989a, 430
1989b; Lotem, 1993; Lotem et al., 1995; Rothstein, 1982). Costs of new defences include 431
developing the cognitive or morphological structures for new defences as well as the added risk 432
of expressing those defences (e.g. rejecting own eggs), and these costs influence the evolution of 433
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16
recognition abilities. Plasticity in host recognition reveals the importance that making an 434
incorrect choice can have for the evolution of egg rejection. For example, some common cuckoo 435
hosts avoid rejecting their own eggs (recognition error) when parasites are not present by only 436
increasing rejection rates when adult cuckoos are seen in the vicinity of the nest (Davies and 437
Brooke 1988). In South American coots, intraspecific parasitism leads to egg rejection, but an 438
interspecific parasite , the blackheaded duck, that imposes no parental care costs is only rejected 439
when ecological conditions render incubation more costly (Lyon and Eadie, 2004). Globally, 440
studies of avian brood parasites have provided an excellent understanding of the selective 441
environment generated by host-parasite interactions that influences the evolution of recognition 442
and rejection of eggs. 443
444
Mimicry-recognition-rejection arms races reveal the link between cognitive abilities and the 445
evolutionary dynamics of host-parasite systems. Arms races in avian brood parasites related to 446
egg mimicry push host recognition systems to identify parasites while avoiding recognition 447
errors (Davies and Brooke, 1988; Rothstein, 1982). The accuracy of identifying a mimetic egg 448
depends on visual discrimination abilities and recent studies have begun to specifically integrate 449
this process using ‘visual modelling’–information on cone sensitivity and objective measures of 450
egg colour patterns–to understand rejection behaviour, or the lack thereof, in some species 451
(Cassey et al., 2008; Spottiswoode and Stevens, 2010). Recent findings show that visual 452
detection of parasites can improve by integrating multiple sources of information (Spottiswoode 453
and Stevens 2010). Egg cues (de la Colina et al., 2012; Langmore et al., 2009; Spottiswoode and 454
Stevens, 2010; Svennungsen and Holen, 2010), external cues of parasite presence (Davies and 455
Brooke, 1988), or counting the number of eggs laid (Lyon, 2003) have all been shown as means 456
to improve the decision to reject parasite eggs. Use of multiple and disparate cues to improve the 457
accuracy of rejection behaviour would require executive functions to weigh these different 458
criteria in a rejection decision and future research could examine this cognitive ability. Not all 459
host species reject eggs or chicks, which implies that physiological or cognitive limitations may 460
also influence the detection of a parasitic egg (Davies and Brooke, 1988; Lotem, 1993; Lotem et 461
al., 1995; Rodríguez-Gironés and Lotem, 1999; Rothstein, 1982). 462
463
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17
An understanding of the cognitive mechanisms underlying rejection have also played an 464
important role in understanding why despite close visual mimicry in eggs, nestlings are rarely 465
mimetic. One hypothesis is that unlike egg recognition where comparisons between multiple host 466
eggs and a single parasitic egg makes discrimination possible, having only a single parasite chick 467
in the nest (e.g. common cuckoos) could have severe long term fitness costs if birds learn the 468
appearance of their chicks (Lotem, 1993). Indeed, learning does seem to play a role in 469
identification and discrimination of eggs (Rothstein, 1974, 1978; Strausberger and Rothstein, 470
2009) and possibly chicks (Colombelli-Négrel et al., 2012; Shizuka and Lyon, 2010). A possible 471
solution in some species, such as the North American coot, might be to use extra cues such as 472
hatch order and soft rejection (e.g. lower feeding) to help identify parasitic chicks while reducing 473
the risk of mis-imprinting (Shizuka and Lyon, 2010, 2011). These models and empirical results 474
show that the cognitive mechanisms underlying how a species is able to recognize its eggs and 475
chicks plays an important role in the evolution of the host-parasite arms race. 476
477
Finally, a few studies have also begun to investigate the link between neurophysiology and the 478
ecology of brood parasites. Initial studies focused primarily on whole brain size or hippocampus 479
size in brood parasites and their non-parasitic relatives since each species should face different 480
ecological imperatives. Generally, whole brain size tends to be smaller in brood-parasites than 481
their closest relatives (Corfield et al., 2013; Iwaniuk, 2004; Overington, 2011), which could be 482
linked to less complex cognitive function needed in the absence of parental care in brood 483
parasites (Boerner and Krüger, 2008). Hippocampus size, however, varies predictably with the 484
need for excellent spatial memory in brood parasites. Brood parasites have an enlarged 485
hippocampus in the breeding season (Clayton et al., 1997), the sex that searches for nests tends 486
to have a larger hippocampus than the other sex (Reboreda et al., 1996; Sherry et al., 1993), and 487
brood parasites have a relatively larger hippocampus than closely related non-parasites (Corfield 488
et al., 2013; Reboreda et al., 1996). Furthermore, recent analysis has uncovered a specific region 489
of the hippocampus that is enlarged in parasitic species relative to others (Nair-Roberts et al., 490
2006), suggesting brain regions may have evolved to manage the specific needs of brood 491
parasites relative to other spatial memory. These studies provide a rare example of direct linkage 492
between ecology and neurophysiology on a well understood fitness landscape. An exciting next 493
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18
step in such systems could be to examine variation in neural structure with variation in the ability 494
of different hosts – either across or within a species – to reject parasitic eggs or chicks. 495
496
The above studies provide some of the best examples of how discrimination ability links with 497
cognitive decision making under natural ecological conditions. While many of these host-498
parasite studies have not specifically been framed in terms of cognitive ecology, the focus on 499
discrimination, recognition, learning, and decision making are all clearly linked to cognition and 500
could further link to both specific cognitive abilities studied in other organisms and to 501
neurophysiological studies. Together with the strong understanding of the fitness costs and 502
benefits of host-parasite coevolution, these systems provide an excellent opportunity to link 503
cognition, neurophysiology, and evolutionary biology. 504
505
Conclusion 506
We have highlighted two ways to investigate the evolution of cognitive processes in animals: the 507
comparative approach focuses on evolutionary history while the fitness approach examines 508
contemporary selection. Much of our knowledge on the evolution of cognition comes from the 509
comparative approach and the full application of recently developed phylogenetic tools should 510
allow for interesting new results in this line of research. However, since cognition presents all 511
the characteristics of traits under selection (variation, heritability and fitness benefits), we believe 512
that taking the fitness approach to cognitive function will allow us to better explore the 513
evolutionary mechanisms that shape animal minds. Furthermore, the fitness approach more 514
easily allows us to integrate proximate and ultimate factors underlying animal cognition in a 515
single study, as suggested fifty years ago by Tinbergen (Tinbergen, 1963). 516
517
4- Future directions 518
The integration of evolutionary biology with cognitive sciences provides a very promising 519
avenue of research that could revolutionize our understanding of animal mind. Here we 520
highlighted how methods and new research questions in evolutionary biology could contribute to 521
our current understanding of the proximate basis of cognition. We believe the (unranked) top 522
priorities for the future are: 523
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19
1) Identify cognitive functions that are crucial for species currently studied by evolutionary 524
ecologists and behavioural ecologists. 525
2) What are the fitness consequences of cognitive performance in the wild and what are the 526
ecological contexts under which that ability is favoured? 527
3) Are cognitive performance and/or neurocognitive processes consistent across different 528
environments for a given species? Are they consistent for a given individual if we can measure 529
cognitive abilities in the wild? 530
4) Can we create more ecologically relevant cognitive performance tasks that help link cognitive 531
abilities or brain structure to specific ecological challenges? 532
5) What environmental or social factors are associated with the evolution of specific cognitive 533
abilities or neural structures across species and what role do these abilities play in the speciation 534
process? 535
6) Are different cognitive abilities related to each other (i.e. positive correlation or trade-off)? Is 536
there compelling evidence for general intelligence in non-human animals? 537
7) Problem solving is the one “cognitive” task that has been related to fitness in wild animals. 538
However the cognitive mechanisms underlying this task remain unclear (Healy, 2012; Rowe and 539
Healy, 2014; Thornton et al., 2014). What are the fitness benefits of other well characterized 540
cognitive capacities such as visual cognition or associative learning? 541
8) What are the implications of cognitive performance for theory in evolutionary ecology and 542
conversely what does an ecological perspective on cognition tell us about neurocognitive 543
development? 544
545
Acknowledgments 546
This work has been supported by a Fyssen postdoctoral fellowship. 547
548
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Figure captions 846
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Figure 1: Mate choice and cognitive capacities that could hypothetically play a role 849
In bi-parental breeding songbirds, choosing an appropriate mate according to available male 850
stock, previous breeding experience and actual environmental conditions is a behaviour that will 851
have drastic fitness consequences for any female and that is likely to rely on the interplay 852
between various cognitive functions. Recognition of ornaments linked to different male qualities 853
(e.g. good genes, parental care, nest defense, etc.) uses perception (visual and auditory) to detect 854
male signals and categorization to group and identify male quality according to their ornaments 855
(1). The use of previous breeding experience relies on past learning linking male ornaments and 856
reproductive success from previous experiences (2). Mate choice itself, integrates all information 857
available to the female including current ecology, mate options, and past experience supposedly 858
through decision-making mechanisms (3). Finding the chosen mate, once the decision has been 859
taken, probably relies on spatial memory to relocate the territory defended by the chosen male 860
and endogenous attention to detect the chosen male from among the background of other males 861
and environmental features (4). 862
863
864
Figure 2: How to study brain and cognition selection? 865
Ideal evolutionary ecology studies of cognition should integrate socio-ecological (left panel, 1), 866
neurocognitive (middle panel, 2) and fitness (right panel, 3) variables. Such an approach seeks to 867
truly merge behavioural and evolutionary (green background) and cognitive neuroscience 868
(yellow background) methods. As examples: 869
1) Socio-ecological contexts of selection could correspond to natural gradients in sociality 870
(ie: Population density, gregariousness), habitat quality (ie: level of fragmentation, 871
urbanization) and/or distribution of resources (ie: harshness of the environment). 872
Experimental manipulations of ecological factors, such as variation in food 873
supplementation or reintroduction in a novel environment, are of particular interest to 874
isolate ecological causes of selection. 875
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2) Cognitive abilities can be measured in the wild using foraging tasks. This approach has 876
been successfully adapted to measure perception, problem solving, learning, behavioural 877
flexibility and spatial cognition. Such methods rely on individual identification usually 878
mediated by visual tags (i.e. colour rings) or passive integrated transponders (PIT) tags. 879
However, some cognitive functions are difficult to measure in the wild and one may want 880
to have a better control on motivational state and environmental parameters. Short-term 881
period of captivity seems appropriate in such a framework and potentially enable us to 882
use up-to-date psychophysics protocols and equipment developed in comparative 883
cognition labs. Development of embedded cameras or microphones has the potentials to 884
reveal spontaneous cognitive capabilities like tool use, social cognition or vocal 885
communication. Likewise, neurologgers or transmitters enable us to measure brain 886
activity (electroencephalogram, single unit activity) in free ranging wild animals. Spatial 887
and whole brain measurement could also be assessed using MRI or PET devices through 888
short term scanning protocol. 889
3) The fitness benefit is traditionally assessed through evaluation of reproductive success or 890
a measure of survival. Behaviour associated with reproductive success (i.e. mating, 891
parental care) can also be used as proxies of fitness. 892
893
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895
896
not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which wasthis version posted September 1, 2015. . https://doi.org/10.1101/024422doi: bioRxiv preprint
not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which wasthis version posted September 1, 2015. . https://doi.org/10.1101/024422doi: bioRxiv preprint
not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which wasthis version posted September 1, 2015. . https://doi.org/10.1101/024422doi: bioRxiv preprint